System and method for distributed computation using heterogeneous computing nodes

ABSTRACT

This disclosure relates generally to the use of distributed system for computation, and more particularly, relates to a method and system for optimizing computation and communication resource while preserving security in the distributed device for computation. In one embodiment, a system and method of utilizing plurality of constrained edge devices for distributed computation is disclosed. The system enables integration of the edge devices like residential gateways and smart phone into a grid of distributed computation. The edged devices with constrained bandwidth, energy, computation capabilities and combination thereof are optimized dynamically based on condition of communication network. The system further enables scheduling and segregation of data, to be analyzed, between the edge devices. The system may further be configured to preserve privacy associated with the data while sharing the data between the plurality of devices during computation.

PRIORITY CLAIM

This application is a national stage application of InternationalApplication No. PCT/IN 2014/000386, filed Jun. 9, 2014, and claimspriority to Indian Application No. 2095/MUM/2013, filed Jun. 20, 2013,the content of both of which are incorporated by reference in theirentirety.

TECHNICAL FIELD

This disclosure relates generally to the use of distributed system forcomputation, and more particularly, relates to a method and system foroptimizing computation and communication resource while preservingsecurity in the distributed device for computation.

BACKGROUND

Conventional computation involves use of a single computer. However,single computer sometime proves inefficient when performing certaincomplex computations. The limitation of using single computer forcomplex computations is usually resolved by adapting a distributedcomputing environment.

The advancement in networking system enabled computer systems that aredistributed geographically to remain connected and perform as a part ofgrid. With this, the distributed computing techniques were ostensiblyable to solve complex computational problems. However, whenever thedistributed computing environment was observed to encompass the edgedevices which may be constrained in terms of computing power, memory,network bandwidth, as computing resources, it required adoptingefficient communication methods to share the scheduled data for theirsubsequent processing.

While employing idle edge nodes for opportunistic computation, sharingof private data poses a formidable challenge. Hence, ensuring dataprivacy while distributing data in an optimized manner as a part ofcomputation became one of the major concerns to be redressed. Arbitraryprivacy-preserving mechanism can lead to either high computational costor low privacy preservation and requires a method for effective privacypreservation of data to ensure minimal privacy breach while performingdistributed computation in a distributed computing environment.

Understandably, additional computational nodes are generally needed forparallel computation. One can use smart devices as non-dedicatedadditional computational nodes. The smart devices can be usedsuccessfully as computational nodes as they remain idle for a largepercentage of time. However, given that the smart devices areconstrained in terms of computation capabilities, memory and bandwidthusage, and power, these parameters require punctilious considerationbefore engaging them as a part of grid for parallel/distributedcomputing.

SUMMARY

Embodiments of the present disclosure present technological improvementsas solutions to one or more of the above-mentioned technical problemsrecognized by the inventors in conventional systems. For example, in oneembodiment, a device for distributed computation, wherein the device isconfigured to use in addition to backend server nodes, a plurality ofedge devices for optimizing resource usage of resource-constrainedsensors has been disclosed. The device may comprise a cluster monitoringmodule. The cluster monitoring module configured to receive data forcomputation. The data for the computation may be shared with the computenodes including the plurality of edge devices participating in thedistributed computing environment or grid. The plurality of edge devicesmay be constrained in terms of available bandwidth and energy. Thedevice may further comprise a privacy module. The privacy module mayenable privacy measurement by performing sensitivity analysis for thedata and the edge devices. The device may further comprise acommunication module. The communication module may be configured totransfer the data to the plurality of edge devices for efficient loaddistribution during computation. The communication module may also beconfigured to preserve privacy, while the data is transferred to theplurality of edge devices. The communication module may further beconfigured to optimize bandwidth usage and energy consumption during thetransfer of the data over a communication network.

In another embodiment, a system for distributed computation over acommunication network is disclosed. The system, herein, comprises of acluster monitoring module that is configured to receive data forcomputation, a privacy module that is configured to provide privacymeasurement for the data received from the cluster monitoring module andthe communication module that is configured to transfer data forefficient load distribution during computation while ensuring privacypreservation during such data transfer and optimizing bandwidth usageand energy consumption over the communication network. One preferredembodiment of the present disclosure comprises plurality of edge devicesthat interacts with said system for optimizing computation andcommunication resource while preserving security in the distributeddevice environment.

In one significant aspect, one or more of the cluster monitoring module,the privacy module and the communication module may be hosted on one ormore of the plurality of edge devices that are communicating with thesystem over the communication network.

In yet another embodiment, a method for distributed computation, whereinin addition to backend server nodes a plurality of edge devices are usedfor distributed computation, is disclosed. According to the disclosure,the bandwidth and energy usage of communication network for a pluralityof edge devices may be efficiently optimized. Firstly, data may bereceived for computation. The data may be received from at least oneedge device selected from the plurality of edge devices. The method thencomprises segregation of the data into smaller data sets of varyingsize, based on a list of partitions created by a cluster monitoringmodule. The smaller data sets may further be allocated to the pluralityof edge devices for subsequent analysis. Further, bandwidth and energyusage may be optimized during allocation of the smaller data sets to theplurality of devices, while preserving privacy during said allocation.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate exemplary embodiments and, togetherwith the description, serve to explain the disclosed principles.

FIG. 1 illustrates a distributed computation environment in accordancewith an embodiment of the present disclosure.

FIG. 2 illustrates a block diagram illustrating the device in accordancewith an embodiment of the present disclosure.

FIG. 3 illustrates a flow chart illustrating a method in accordance withan embodiment of the present disclosure.

DETAILED DESCRIPTION

Exemplary embodiments are described with reference to the accompanyingdrawings. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears.Wherever convenient, the same reference numbers are used throughout thedrawings to refer to the same or like parts. While examples and featuresof disclosed principles are described herein, modifications,adaptations, and other implementations are possible without departingfrom the spirit and scope of the disclosed embodiments. It is intendedthat the following detailed description be considered as exemplary only,with the true scope and spirit being indicated by the following claims.

The present subject matter discloses a system, device and method fordistributed computation, wherein a plurality of edge devices are usedalong with backend server nodes for effective optimization of resourceusage amongst the resource constrained sensors within a distributedcomputation environment. The present subject matter enables utilizationof processing capability of edge devices by applying a low overheadcommunication and computation mechanism for distributing data, while atthe same time preserving privacy of distributed data. The edge devicesmay be smart phones or Personal Digital Assistants (PDA's) and any suchhand-held device that may be connected using constrained residentialgateways, and may form a part of IoT (Internet of Things). As indicatedabove, the disclosure enables resolving privacy concerns associated withthe distributed computing using edge devices.

In one aspect, the present subject matter enables a user to usecomputation power of edge devices, like smart phone, PDA's orresidential gateways, for distributed computing. The edge devices likeresidential gateways, PDA's and smart phones may integrate into anexisting grid, or form a new grid for distributed computation. Theintegration of edge devices into the grid may enable utilization ofprocessing power of the edges devices that are idle in analyzing data.Utilization of the residential gateways and smart phones may also helpin cost optimization associated with the distributed computation.

According to the present disclosure, the edge devices may be constrainedin terms of bandwidth available for communication or data transfer, andenergy useable to power the edge devices. The communication means andenergy may therefore require optimization. In one aspect, the presentsubject matter discloses privacy preservation mechanism for distributedcomputation using edge devices distributed over a geographical region.

In one aspect, the edge device may be connected to a master node. Themaster node can be defined as a computer node that is configured toreceive data, perform processing and execute preset instructions, havinghigh processing capability. The communication network/gateway betweenthe edge devices and the master node may be constrained in terms ofavailable bandwidth and energy. It shall however be acknowledged thatthere may be one or more master nodes at a given point of time managingmultiple clusters of backend server nodes and edge devices in anhierarchical manner. Also, it shall be understandable that at a giveninstance an edge device may itself act as a master node managing devicesbelow it in an hierarchical order, depending on the application andtheir computational capability.

In one aspect of the disclosure, data is shared between the master nodeand the edge devices such that a low overhead communication andcomputation is achieved. Thus, whenever the data is shared between themaster node and the edge devices 104, irrespective of where the dataoriginates from, a communication means broadcasts the messaging/groupcommunication among the edge devices thereby utilizing available networkbandwidth optimally. The communication means may further compress thedata by randomized network encoding technique to optimize networkbandwidth and energy usage during such data transfer to the edgeddevices. The energy usage for the edge devices may also be optimized byestablishing a relation between the length of coded data and number ofedge devices receiving the data in a distributed environment. Thecommunication means of the present disclosure thus enables reduction innetwork traffic, collaboration amongst the participating edge nodes anddynamic utilization of unused network capacity.

The master node according to the disclosure may be defined as a computernode with high processing capability, configured to receive data,perform processing and execute pre-set instructions. As will beacknowledged by those skilled in the art, there may be one or moremaster nodes at a given point of time managing multiple clusters ofbackend server nodes and edge devices in an hierarchical manner.Further, an edge device may itself act as a master node at certainpoints of time managing devices below itself in the hierarchy dependingon the application and their computational capability. The master nodebased on permission granted by the user of the edge device may beconfigured to capture information pertaining to usage pattern of theedge device, computation speed, memory size, network connectivity andload request from the edge device. The master node can also be furtherconfigured to provide feedback to the user of the edge deviceparticipating to form the grid about their usage pattern and statisticsof grid activity. The master node may further be configured to estimatecomputation capability of edge device based on the information captured.The master node based on given task deadline for the computation maycreate a list of partitions and dataset size for analysis by each ofavailable edge device in the grid. The list of partitions and datasetsize created by the master node may also take into consideration usagepattern based prediction, and location of the edge device.

According to the present disclosure, the master node may further beenabled to detect edge node failure. The master node may furtherpartition data into smaller data sets. These smaller data sets can thenbe analysed in parallel by the available edge devices. The master nodeaccording to an embodiment of the present disclosure may schedule thecomputation of the smaller data sets using the communication means inorder to optimize the load distribution with bandwidth and energy usage.On completion of said computation, the master node may combine finaldata sets received from the edge devices to output the final result.

According to an embodiment of the present disclosure, and as discussedwhenever the master node distributes/segregates data betweenparticipating edge devices for computation, the privacy of the data maybe required to be preserved/protected. Based on the privacy requirementof the data a privacy preserving method, selected from data aggregation,or perturbation, may be employed.

According to an embodiment of the present disclosure, the privacyrequirement/measurement may be obtained by performing analysis on thecomputation capability of the edge device, the privacy-utilityrequirement and sensitive content of the data. While the privacyrequirement is gathered from the data source in an interactive manner,the utility requirements are gathered from data sink or the end user inan interactive manner.

According to an embodiment of the present disclosure, if privacy-utilityrequirement and sensitivity analysis of the data indicate higherstrength privacy preservation (equivalent to more data perturbation),preferably an edge device with higher computational power is chosen tocarry out such operation. If engaging such an edge device is notfeasible, then the privacy preservation scheme is optimized in a mannerto match the computational capability of best possible edge device. Inanother extreme, when computational requirement of privacy preservationscheme is low, low computational powered edge device can be chosen.

While aspects of described device, and method for distributedcomputation, wherein a plurality of edge devices are used fordistributed computation may be implemented in any number of differentcomputing systems, environments, and/or configurations, the embodimentsare described in the context of the following exemplary device.

Referring now to FIG. 1, a distributed computation environment 100 for amaster node 102 is illustrated, in accordance with an embodiment of thepresent subject matter. In one embodiment, the master node 102 enablesthe distribution of data within a distributed computation environment.The master node 102 according to an embodiment utilizes non-dedicatededge devices such as residential gateways and smart phones forprocessing the data.

Although the present subject matter is explained considering that themaster node 102 is implemented on a server, it may be understood thatthe master node 102 may also be implemented in a variety of computingsystems, such as a laptop computer, a desktop computer, a notebook, aworkstation, a mainframe computer, a server, a network server, and thelike. Further, the master node can reside in smart phone or otherportable device having higher computation capabilities. It will beunderstood that the master node 102 may be accessed by multiple usersthrough one or more edge devices 104-1, 104-2 . . . 104-N, collectivelyreferred to as edge devices 104 hereinafter, or applications residing onthe edge devices 104. Examples of the edge devices 104 may include, butare not limited to, a portable computer, a personal digital assistant, ahandheld device, and a residential gateway. The edge devices 104 arecommunicatively coupled to the master node 102 through a communicationnetwork 106. In one implementation, the communication network 106 may bea wireless network, a wired network or a combination thereof. Thecommunication network 106 can be implemented as one of the differenttypes of networks, such as intranet, local area network (LAN), wide areanetwork (WAN), the internet, and the like. The communication network 106may either be a dedicated network or a shared network. The sharednetwork represents an association of the different types of networksthat use a variety of protocols, for example, Hypertext TransferProtocol (HTTP), Transmission Control Protocol/Internet Protocol(TCP/IP), Wireless Application Protocol (WAP), and the like, tocommunicate with one another. Further the communication network 106 mayinclude a variety of network devices, including routers, bridges,servers, computing devices, storage devices, and the like.

According to another implementation the communication network 106 may bea constrained communication network; the constraints may relate tobandwidth and energy available to the communication network 106. Theconstrained communication network according to an implementation may beselected from residential gateways, or smart phone gateways.

Referring now to FIG. 2, a master node 102 is illustrated in accordancewith an embodiment of the present subject matter. In one embodiment, themaster node 102 may include at least one processor 202, an input/output(I/O) interface 204, and a memory 206. The at least one processor 202may be implemented as one or more microprocessors, logic circuitries,and/or any devices that manipulate signals based on operationalinstructions. Among other capabilities, the at least one processor 202is configured to fetch and execute computer-readable instructions storedin the memory 206.

The I/O interface 204 may include a variety of software and hardwareinterfaces, for example, a web interface, a graphical user interface,and the like. The I/O interface 204 may allow the master node 102 tointeract with a user directly or through the edge devices (Not Shown).Further, the I/O interface 204 may enable the master node 102 tocommunicate with other devices using the communication network 106. TheI/O interface 204 can facilitate multiple communications within a widevariety of networks and protocol types, including wired networks, forexample, LAN, cable, etc., and wireless networks, such as WLAN,cellular, or satellite. The I/O interface 204 according to an embodimentmay perform as the constrained communication network 106; theconstraints may relate to bandwidth and energy available to thecommunication network 106.

The memory 206 may include any computer-readable medium known in the artincluding, for example, volatile memory, such as static random accessmemory (SRAM) and dynamic random access memory (DRAM), and/ornon-volatile memory, such as read only memory (ROM), erasableprogrammable ROM, and flash memories. The memory 206 may include modules208 and data 224.

The modules 208 include routines, programs, objects, components, datastructures, etc., which perform particular tasks or implement particularabstract data types. In one implementation, the modules 208 may includea cluster interface module 210, a cluster monitoring module 212, a datapartitioning module 214, a core scheduler module 218, a combiner module224 a privacy module 220, a communication module 216 and other modules222. The other modules 222 may include programs or coded instructionsthat supplement applications and functions of the master node 102.

The data 224, amongst other things, serves as a repository for storingdata processed, received, and generated by one or more of the modules208. The data 224 may also include other data 226. The other data 226may include data generated because of the execution of one or moremodules in the other module 222.

According to an embodiment of the present disclosure, a grid fordistributed computing may be created using residential gateways andsmart phones as edge devices 104. The present embodiment enables tappingof the processing power of the edges devices 104 for distributedcomputing. The distributed computing using edges device such as smartphones and residential gateways enable the user to harness capacity ofnon-dedicated edge devices for computing thereby reducing the cost andtime consumed for computation. The communication module 216 optimizesusage of available bandwidth and energy of the edge devices 104 therebyenabling the use of residential gateways and smart phones as edgedevices 104. The communication module 216 may compress the data,requiring computation, by randomized coding to optimize the datatransfer. Further, the communication module 216 may optimize the energyusage of the edge devices 104 by preserving relation between length ofcoded data and number of edge devices 104 receiving the data in adistributed environment. The communication module 216 may further beconfigured to dynamically utilize, unused network capacity of theconstrained communication network 106.

According to embodiment of the present disclosure the cluster interfacemodule 210 may be configured to capture information pertaining to theedge devices 104 participating in the grid generation. The informationmay pertain to computation speed, RAM size, CPU and RAM usage, networkconnectivity and load requested by the user. The cluster interfacemodule 210 may be further configured to provide real-time or delayedfeedback about usage of network connectivity and computation for theedge devices to users of the edge devices.

According to another embodiment the cluster interface module 210 mayreside in the edge devices 104 and can be executed from the edge devices104 upon request received from either the master node 102 or the users.

The cluster interface module 210, of the present device further providesthe information captured by the cluster monitoring module 212. Thecluster monitoring module 212, receives the information pertaining toedge devices in the grid. Based on the information captured the clustermonitoring module 212 estimates computation capabilities of the nondedicated edge devices. The cluster monitoring module 212 may further beconfigured to detect failures of edge devices during computation of adata. The cluster monitoring module 212 receives the data requiringcomputation. The cluster monitoring module 212 further creates apartition list for the data and maps the partition list with availableedge devices 104. The cluster monitoring module 212 captures usagepatterns of the edge devices 104 and updates the grid as and when newparticipating edge devices 104 join in.

The data partitioning module 214, segregates the data received by thecluster monitoring module 212 into smaller data sets. The segregation ofthe data into smaller data sets enables optimizing the bandwidth usagefor the edge devices and also appropriates mapping of the data set witheach edge device available based upon the list generated by the clustermonitoring module 212. The smaller data sets may vary in size. The sizeof the data set may be governed by the computation capability of theplurality of edge devices 104, and the network bandwidth available tothe plurality of the edge devices 104 and other network channelcharacteristics.

The core scheduler module 218, upon receiving the smaller data setschedules the computation based on the availability of edge devices andtime for computation. The combiner module 218 then eventually generatesfinal result of the computation. The final result is based upon theaggregation of analysed data sets received from the edge devices uponcompletion computation.

According to the embodiment, the privacy module 220 may be configured toprovide privacy measurement. The privacy measurement may be determinedby performing analysis on the computation capability of the edge device,the privacy-utility requirement and sensitive content of the data.Further, the privacy module 220 enables complete privacy preservation orselective privacy preservation. The privacy preservation may be basedupon computation capability of the at least one edge device from theplurality of edge devices, privacy-utility requirement, and thesensitivity of data.

Now referring to FIG. 3, the flow chart illustrates a method fordistributed computation 300, wherein a plurality of edge devices areused for distributed computation in accordance with an embodiment of thepresent disclosure. At block 302, data is received at the mater node 102by the cluster monitoring module 212. The data received may originatefrom at least one edge device selected from the plurality of edgedevices 104.

Next, at block 304 the data received for computation is segregated intosmaller data sets by the data partitioning module 214. The smaller datasets may vary in size. The size of the data set may be governed by thecomputation capability of the plurality of edge devices estimated by thecluster monitoring module 212, and network characteristics such as thebandwidth available to the plurality of the edge devices and round triplatency, estimated by the communication module 216. At block 306 thesmaller data sets are allocated to the plurality of edge devices by thecore scheduler module 218. The core scheduler module 218 enablesscheduling for the computation of the data based on the list ofpartitions and the size for the smaller data sets created by the clustermonitoring module 212. The allocation further comprises scheduling ofthe data schedule by core scheduler module 216 for analysis based on thelist of partitions, available edge devices, available bandwidth, andother network channel characteristics such as latency. This furtherenables appropriate privacy preservation thereby maintaining arelationship among the size of scheduled data, network channelcharacteristics (such as bandwidth, latency), state of the device (idle,active), corresponding available energy levels, and appropriate privacypreservation scheme.

At block 308, bandwidth and energy usage of the plurality of edgedevices is optimized for efficient computing during the allocation. Thisfacilitates exchange of information amongst the plurality of edgedevices 104 in the master node 102, between the edge devices and themaster node 102, and amongst the edge devices 104 themselves as aprocess of collaborative computing. According to an exemplaryembodiment, the optimization for usage of the bandwidth and energy isachieved by enabling efficient group communication scheme (broadcastingthe messaging/group communication) among the edge devices 104 therebyutilizing available network bandwidth optimally. Further optimizationmay achieved by compressing the data via randomized network encodingtechnique prior to data transmission.

According to the exemplary embodiment, energy usage for the edge devices104 and efficient load distribution during data compression may beachieved by preserving a relation between length of coded data andnumber of edge devices receiving the data in distributed environment.

Although implementations for method and device for enabling resourceusage optimization of resource constrained sensors have been describedin language specific to structural features and/or methods, it is to beunderstood that the appended claims are not necessarily limited to thespecific features or methods or advantages described. Rather, thespecific features and methods are disclosed as examples ofimplementations for distributed computing environments engagingplurality of edge devices.

The illustrated steps are set out to explain the exemplary embodimentsshown, and it should be anticipated that ongoing technologicaldevelopment will change the manner in which particular functions areperformed. These examples are presented herein for purposes ofillustration, and not limitation. Further, the boundaries of thefunctional building blocks have been arbitrarily defined herein for theconvenience of the description. Alternative boundaries can be defined solong as the specified functions and relationships thereof areappropriately performed. Alternatives (including equivalents,extensions, variations, deviations, etc., of those described herein)will be apparent to persons skilled in the relevant art(s) based on theteachings contained herein. Such alternatives fall within the scope andspirit of the disclosed embodiments. Also, the words “comprising,”“having,” “containing,” and “including,” and other similar forms areintended to be equivalent in meaning and be open ended in that an itemor items following any one of these words is not meant to be anexhaustive listing of such item or items, or meant to be limited to onlythe listed item or items. It must also be noted that as used herein andin the appended claims, the singular forms “a,” “an,” and “the” includeplural references unless the context clearly dictates otherwise.

Furthermore, one or more computer-readable storage media may be utilizedin implementing embodiments consistent with the present disclosure. Acomputer-readable storage medium refers to any type of physical memoryon which information or data readable by a processor may be stored.Thus, a computer-readable storage medium may store instructions forexecution by one or more processors, including instructions for causingthe processor(s) to perform steps or stages consistent with theembodiments described herein. The term “computer-readable medium” shouldbe understood to include tangible items and exclude carrier waves andtransient signals, i.e., be non-transitory. Examples include randomaccess memory (RAM), read-only memory (ROM), volatile memory,nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, andany other known physical storage media.

It is intended that the disclosure and examples be considered asexemplary only, with a true scope and spirit of disclosed embodimentsbeing indicated by the following claims.

We claim:
 1. A system for distributed computation in a communicationnetwork, the system comprising: a cluster monitoring module configuredto receive data for computation; a privacy module configured to provideprivacy measurement by performing sensitivity analysis on the datareceived by the cluster monitoring module; and a communication moduleconfigured to transfer the data for efficient load distribution duringcomputation, and to preserve privacy, wherein the communication moduleis further configured to optimize bandwidth usage and energy consumptionduring the transfer of the data over the communication network.
 2. Thesystem of claim 1, further comprising a plurality of edge devices,wherein one or more of the cluster monitoring module, the privacymodule, and the communication module are hosted on one or more of theedge devices among the plurality of edge devices.
 3. The system of claim2, wherein the plurality of edge devices further comprises: a processor;a memory coupled to the processor, wherein the memory comprises theclustering module, the privacy module, and the communication module. 4.The system of claim 1, wherein the communication module is furtherconfigured to reduce traffic via group communication based communicationscheme, to collaborate among the plurality of edge devices, and todynamically utilize unused network capacity of the communicationnetwork.
 5. The system of claim 1, wherein the bandwidth usage andenergy consumption is optimized by compressing data by network encodingthe data prior to the transfer of data, and formulating a relationshipbetween length of coded data and number of edge devices from theplurality of edge devices.
 6. The system of claim 1, wherein the privacymodule further enables privacy preservation, wherein the privacypreservation is based on at least one factor selected from computationcapability of the at least one edge device from the plurality of edgedevices, privacy-utility requirement, and sensitivity of the data. 7.The system of claim 1, further comprising a partitioning moduleconfigured to segregate the data into smaller data sets, wherein thesmaller data sets are analyzed simultaneously by the plurality of edgedevices.
 8. The system of claim 7, wherein size of the smaller data setsis governed by the computation capability of the plurality of edgedevices, and network characteristics, wherein the networkcharacteristics includes one or more of bandwidth available to theplurality of the edge devices, round trip latency.
 9. The system ofclaim 1, wherein the cluster monitoring module is further configured tocreate a list of partitions and the size for the smaller data sets, anddetect failure of edge devices from the plurality of edge devices. 10.The system of claim 1, further comprising a core scheduler module,wherein the core scheduler module enables scheduling for the computationof the data, wherein the scheduling of the data for computation is basedon the list of partitions and the size for the smaller data sets createdby the cluster monitoring module.
 11. A device for distributedcomputation, wherein the device is configured to engage a plurality ofedge devices over a communication network, the device comprising: aprocessor; a memory coupled to the processor, wherein the memorycomprises a plurality of modules capable of being executed by theprocessor, and wherein the plurality of modules comprising: a clustermonitoring module configured to receive data for computation; a privacymodule configured to provide privacy measurement by performingsensitivity analysis on the data received by the cluster monitoringmodule; and a communication module configured to transfer the data tothe plurality of edge devices for efficient load distribution duringcomputation, and to preserve privacy, wherein the communication moduleis further configured to optimize bandwidth usage and energy consumptionduring the transfer of the data over a communication network.
 12. Amethod for distributed computation, wherein a plurality of edge devicesare used for distributed computation along with a backend server, themethod comprising: receiving data for computation from at least one edgedevice selected from the plurality of edge devices; segregating the datainto smaller data sets of varying size, based on a list of partitionscreated; allocating the smaller data sets to the plurality of edgedevices for subsequent analysis; and optimizing bandwidth and energyusage during allocation of the smaller data sets to the plurality ofdevices, while preserving privacy during said allocation.
 13. The methodof claim 12, further comprises detecting failure of a edge device fromthe plurality edge devices during the analyses.
 14. The method of claim12, wherein the smaller data sets of varying size are obtained based onthe computation capability of the plurality of edge devices, and networkcharacteristics such as the bandwidth available to the plurality of theedge devices and round trip latency.
 15. The method of claim 12, whereinthe bandwidth and the energy usage are optimized by enabling datatransfer amongst the edge devices at the backend server, between theedge devices and the backend server and amongst the edge devices. 16.The method of claim 12, wherein the bandwidth and the energy usage arefurther optimized by network encoding the data to compress the dataprior to the transfer of data, and formulating a relationship betweenlength of coded data and number of edge devices from the plurality ofedge devices.
 17. The method of claim 12, wherein the allocation furthercomprises, scheduling of the data set for analysis based on the list ofpartitions, available edge devices, available bandwidth, other networkchannel characteristics, and desired level of privacy preservation.